Didn't see your reply yet, Mathieu :)
Thanks
2013/7/2 Jaques Grobler <[email protected]>
> Ah when I looked further I see you got some answers here too
>
>
> http://metaoptimize.com/qa/questions/13329/regression-task-trained-on-binary-labels
>
>
>
>
> 2013/7/2 Jaques Grobler <[email protected]>
>
>> I would think that Logistic Regression[1] could apply here.. You can feed
>> it binary labels and then it will act as a classifier that will return for
>> each label the conditional class probability values .
>>
>> See [2] for scikit-learns implementation
>>
>> [1] http://en.wikipedia.org/wiki/Logistic_regression
>>
>> [2]
>> http://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
>>
>> Hope it helps :)
>>
>>
>>
>> 2013/7/1 Gene Kogan <[email protected]>
>>
>>> I have a regression task where I have to assign a continous label
>>> between 0 and 1, but my training set contains only binary labels, 0s and
>>> 1s. Should I treat this as a classification problem and map the labels to
>>> a continous line via some confidence metric (if it's available) or is there
>>> a regression algorithm which can be trained on binary labels? What
>>> scikits-learn methods will help me achieve this? Thanks!
>>>
>>> best,
>>> gene
>>>
>>>
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